Salmon Run

As a Software Engineer (backend Web Development then Search) turned Data Scientist, I was particularly interested in what the book Software Engineering for Data Scientists by Andrew Treadway had to say about the reverse transition. Transitioning between sub-disciplines is a given in our industry -- I started life as a sales/support engineer, then moved to application programming, then back and

The Attention Is All You Need paper proposed the Transformer Architecrture as an improvement to the dominant encoder-decoder models of the time (both recurrent and convolutional). These models used an attention mechanism to connect the encoder and decoder parts, but the Transformer Architecture flipped the script, putting the Attention Mechanism at the center. An early implementation of the

I attended PyData Global 2025 earlier this month. I had hoped to write this up earlier, but I've been busy, so only now getting the time Christmas morning. Merry Christmas to all my readers and best wishes for a Happy New 2026, hopefully it will be even better and more exciting (on the technology front) than this one! Taking stock of this year earlier today, I think I have some serious catching

As someone who primarily works in NLP and Search in the Health Domain, I don't have much use for Time Series. However, while exploring the Financial domain based on personal interest, I have been curious about Time Series for some time. Recently I attended the OpenHPI course Time Series Analysis taught by Mario Tormo Romero (even did the quizzes and the certificate of completion!). I was familiar

I recently read Statistics every Programmer Needs by Gary Sutton. I am probably a good target audience for the book since I used to be a software developer that transitioned into data science some 10 years ago, then into machine learning with neural networks and transformers, and more recently, to Generative AI with Large Language Models. During this time, I have read numerous books on statistics

For those in similar professional circles as I am in, i.e. looking forward into the Generative AI space, yet with one foot pragmatically and firmly stuck in Machine Learning (ML) and Deep Learning (DL) techniques of the (recent, ok, not very distant) past, you will find Dr Amita Kapoor's recent book Hands-On Artificial Intelligence for IoT: Expert Machine Learning and Deep Learning Techniques for

Coming from a background of Knowledge Graph (KG) backed Medical Search, I don't need to be convinced about the importance of manually curated structured knowledge on the quality of search results. Traditional search is being rapidly replaced with Generative AI using a technique called Retrieval Augmented Generation (RAG), where the pipeline produces an answer summarizing the search results

As an ML Engineer, we are generally tasked with solving some business problem with technology. Typically it involves leveraging data assets that your organization already owns or can acquire. Generally, unless it is a very simple problem, there would be more than one ML model involved, maybe different types of models depending on the sub-task, maybe other supporting tools such as a Search Index

I attended PyData Global 2024 last week. Its a virtual conference, so I was able to attend it from the comfort of my home, although presentations seem to be scheduled to be maximally convenient, time-wise, for folks in the US East Coast and Western Europe, so some of them were a bit early for me. There were four main tracks -- the General Track, the Data / Data Science Track, the AI / ML track

Retrieval Augmented Generation (RAG) has become a popular approach to harness LLMs for question answering using your own corpus of data. Typically, the context to augment the query that is passed into the Large Language Model (LLM) to generate an answer comes from a database or search index containing your domain data. When it is a search index, the trend is to use Vector search (HNSW ANN based)

I recently came across Prompt Compression (in the context of Prompt Engineering on Large Language Models) on this short course on Prompt Compression and Query Optimization from DeepLearning.AI. Essentially it involves compressing the prompt text using a trained model to drop non-essential tokens. The resulting prompt is shorter (and in cases of the original context being longer than the LLM's

Couple of weeks ago a colleague and I participated in an internal hackathon where the task was to come up with an interesting use case using the recent multi-modal Large Language Models (LLMs). Multi-modal LLMs take not only text inputs via their prompt like earlier LLMs, but can also accept non-text modalities such as images and audio. Some examples of multi-modal LLMs are GPT-4o from OpenAI,

Unlike many Data Scientists, I didn't automatically reach for Pandas when I needed to analyze data. I came upon this discipline (Data Science) as a Java Software Engineer who used Python for scripting, so I was quite comfortable operating on JSON / CSV / text files directly, loading data into relational databases and running SQL against them, and building visualizations with Matplotlib. So when

Last month, I decided to sign-up for the Google AI Hackathon, where Google provided access to their Gemini Large Language Model (LLM) and tasked participants with building a creative application on top of it. I have worked with Anthropic's Claude and OpenAI's GPT-3 at work previously, and I was curious to see how Gemini stacked up against them. I was joined in that effort by David Campbell and

I've been working on an application where, given an input string, the objective is to recommend an output string that is similar to the input string, for some notion of similarity. A machine learning model, in this case a SentenceTransformers model, is taught this notion of similarity by showing it many examples of input-output pairs. The model's weights are then used to encode the part to be

I was at KGC (Knowledge Graph Conference) 2024, which is happening May 6-10 at Cornell Tech. I was presenting (virtually) at their Health Care and Life Sciences (HCLS) workshop, so my speakers pass was only valid for today for the HCLS portion of KGC. My trip report covers a few talks that I attended here. Attending virtually was a bit chaotic as sessions went over sometimes, so you might leave a

Drug Discovery is a field where biochemists (and more recently computer scientists) turn ideas into potential medications. I first came across a few applications in this area when checking out how to build Graph Neural Networks (GNN) as part of auditing the CS224W: Machine Learning with Graphs course from Stanford, some learnings of which I recycled into my Deep Learning with Graphs tutorial at

At our weekly This Week in Machine Learning (TWIML) meetings, (our leader and facilitataor) Darin Plutchok pointed out a LinkedIn blog post on Semantic Chunking that has been recently implemented in the LangChain framework. Unlike more traditional chunking approaches that use number of tokens or separator tokens as a guide, this one chunks groups of sentences into semantic units by breaking them

I got into Natural Language Processing (NLP) and Machine Learning (ML) through Search. And this led me into Generative AI (GenAI), which led me back to Search via Retrieval Augmented Generation (RAG). RAG started out relatively simple -- take a query, generate search results, use search results as context for a Large Language Model (LLM) to generate an abstractive summary of the results. Back

I have read Allen Downey's books on statistics in the past, when trying to turn myself from a Software Engineer into what Josh Wills says a Data Scientist is -- someone who is better at statistics than a Software Engineer and better at software than a statistician (with somewhat limited success in the first area, I will hasten to add). Last year, I had the good fortune to present at PyData Global

Most of us are familiar with Named Entity Recognizers (NERs) that can recognize spans in text as belonging to a small number of classes, such as Person (PER), Organization (ORG), Location (LOC), etc. These are usually multi-class classifier models, trained on input sequences to return BIO (Begin-Input-Output) tags for each token. However, recognizing entities in a Knowledge Graph (KG) using this

I had the opportunity to present at PyData Global this year. It is a virtual conference that ran over 3 days in multiple tracks from December 6 to 8. I talked about Building Learning to Rank models for search using Large Language Models. For those attending the conference, I already shared the links to the slides and the associated code on its Discord channel, but for those who are not, they are

Generative AI has been the new cool kid on the AI / ML block since early this year. Like everyone else, I continue to be amazed and wowed with each successive success story as they break existing benchmark records and showcase novel applications built on top of their new functionality. I was also lucky to be involved in a Generative AI project since the middle of this year, which gave me access

I recently needed to build an Apache Spark (PySpark) job where the task was (among other things) to use a Language Model (LM) to encode text into vectors. This is an embarassingly parallel job where the text to encoding is one to one, so something like Spark works very well here. We could, in theory at least, achieve a N-fold performance improvement by horizontally partitioning the data into N

Here is Part IV of my ongoing review of the Biomedical Artificial Intelligence (BMI 702) course, part of Harvard's Foundation of Biomedical Informatics 2023 Spring session, taught by Prof Marinka Zitnik and her team. If you want to check out my previous reviews in this series, they are listed below. BMI 702 Review Part I BMI 702 Review Part II (Graph Learning) BMI 702 Review Part III (